When relying on distinctive image features like SIFT [1]}, SURF [2]} or ORB [3]} features, a unique matching between two image points can be found. However, such image features can only be calculated in descriptive image regions, resulting in a very sparse correspondence field. Thus, in the case of dense image matching eq:imagematching is evaluated for each pixel in the reference image, computing a pixel-wise matching cost \(s\)  according to \(\Phi \) , which indicates the similarity between the pixel pair. This cost is then stored inside a three-dimensional cost volume \(\mathcal {S}\)  from which the disparity map is later extracted. In our work, we have implemented the two most commonly used similarity measures for real-time dense image matching, namely the Hamming distance of the non-parametric *CT [4]} and the *NCC. Since the Hamming distance of the *CT is minimal when both image patches are most similar, it can directly be used as the matching cost \(s_\mathrm {CT}\) . The *NCC, however, is equal to 1 when both image patches are equal. Thus, the *NCC is inverted and truncated before being evaluated as matching cost: \(s_\mathrm {NCC} = 1 - \max {\left(0, \Phi _\mathrm {NCC}\right)}\) .
